Optimizing Abstract Abstract Machines

TL;DR

This paper presents a systematic process to transform naive analyzers derived from abstracting abstract machines into highly efficient and correct implementations, significantly improving performance while maintaining soundness.

Contribution

It introduces a step-by-step method to optimize analyzers from AAM, achieving substantial performance gains over systematically derived analyzers.

Findings

Achieved 100-1000x speedup over naive analyzers

Maintained soundness and correctness of the optimized analyzers

Produced analyzers competitive with hand-optimized implementations

Abstract

The technique of abstracting abstract machines (AAM) provides a systematic approach for deriving computable approximations of evaluators that are easily proved sound. This article contributes a complementary step-by-step process for subsequently going from a naive analyzer derived under the AAM approach, to an efficient and correct implementation. The end result of the process is a two to three order-of-magnitude improvement over the systematically derived analyzer, making it competitive with hand-optimized implementations that compute fundamentally less precise results.